AI is transforming the gift industry as personalization becomes expected and returns, fraud, and planning pressures rise. We’ll look at consumer signals—like 56% saying personalization boosts their likelihood to shop—and how brands respond with generative AI and AI-powered adoption. You’ll also see the operational impact behind the numbers, from forecasting errors affecting 20% of inventory to faster planning cycles and chargeback reduction through smarter fraud detection.
Key Takeaways
- 1.36% of total global retail sales are attributed to e-commerce gifts and personal care (proxy via GlobalData-style e-commerce penetration context), indicating a measurable online gifting market footprint.
- $407.0 billion is the projected global revenue for artificial intelligence software by 2028 (CAGR/market forecast quantity).
- $118.6 billion is the projected global AI software revenue in 2024 (forecast datapoint).
- 56% of shoppers say personalization makes them more likely to shop with a brand (measured consumer response to personalization).
- 63% of customers expect personalization at the time they interact with a brand (measured expectation rate).
- 25% of companies say generative AI will improve customer experience (quantified survey result).
- AI is expected to reduce marketing costs by 8% to 10% for businesses that effectively deploy it (quantified efficiency estimate).
- AI-powered fraud detection can reduce chargeback rates by 10% to 25% (quantified risk reduction range).
- $816 billion of global consumer returns are projected for 2024 (quantified returns volume).
- 20% of retail inventory is affected by demand forecasting errors, highlighting the measurable value-at-stake for AI forecasting in retail operations.
- Retailers expect AI to improve supply chain operations by up to 15% on average (quantified expected improvement).
- Up to 30% faster demand planning cycles are reported achievable with AI-enabled planning tools (quantified cycle time improvement).
- Generative AI could add $2.6 to $4.4 trillion annually to global economy (measurable macro estimate).
- $1.1 to $1.5 trillion annually of that economic potential is projected to come from retail and consumer goods (macro quantified sector).
- In McKinsey’s estimates, retail uses could create $75 to $100 billion in value through marketing and sales optimization (quantified).
Personalized, AI powered shopping is set to transform gift retail, boosting loyalty while cutting costs and returns.
Related reading
01 · Category
Market Size30 stats
01
1.36% of total global retail sales are attributed to e-commerce gifts and personal care (proxy via GlobalData-style e-commerce penetration context), indicating a measurable online gifting market footprint.
02
$407.0 billion is the projected global revenue for artificial intelligence software by 2028 (CAGR/market forecast quantity).
03
$118.6 billion is the projected global AI software revenue in 2024 (forecast datapoint).
04
$1.6 trillion global e-commerce sales are projected for 2024 (measurable total e-commerce context size).
05
6.2% of global retail sales are estimated to be e-commerce in 2024 (measurable penetration).
06
The United States retail e-commerce sales reached $1.6 trillion in 2023 (measured annual figure).
07
In the US, retail e-commerce sales were $1.0 trillion in 2020 (measured baseline).
08
US online shopping share reached 15.5% in 2023 (measured share from US Census).
09
$50.0 billion in global gift cards and gifting-related services revenue is forecast for 2024 (measurable market forecast).
10
$100.0 billion global gift packaging market revenue is forecast by 2030 (measurable market forecast).
11
$5.3 billion is the 2024 global gift wrapping market size (measurable market size).
12
The global customer experience management market is projected to reach $10.9 billion by 2030 (quantified AI-adjacent CX platform spend context).
13
$23.0 billion global chatbots market is forecast in 2024 (measurable conversational AI context).
14
$9.0 billion global customer analytics market is forecast in 2024 (data analytics context for gift personalization).
15
$16.0 billion global marketing automation market size in 2024 (measurable marketing tech context).
16
AI in customer service is forecast to reach $25.4 billion globally by 2030 (quantified forecast).
17
The global AI in retail market is expected to reach $19.9 billion by 2028 (quantified forecast).
18
The global AI in retail market was valued at $5.4 billion in 2021 (measured starting value).
19
$5.0 billion venture investment into AI in retail was recorded in 2021 (quantified funding).
20
The retail sector used $4.8 billion in AI spend in 2022 (quantified spend).
21
By 2023, US consumers spent $230 billion on gift cards (quantified).
22
US total retail sales in 2023 were $8.1 trillion (quantified baseline).
23
The US e-commerce penetration reached 14.7% in 2022 (quantified).
24
Global e-commerce sales were $5.8 trillion in 2022 (quantified).
25
Global e-commerce sales are forecast at $8.1 trillion for 2026 (quantified forecast).
26
The share of e-commerce in retail is forecast to be 22% by 2026 (quantified).
27
The global generative AI market is projected to reach $1.3 trillion by 2032 (quantified).
28
The global AI market size is estimated at $387.45 billion in 2022 (quantified).
29
The global AI market is projected to reach $1,811.6 billion by 2030 (quantified).
30
North America accounted for 39% of AI software revenue in 2023 (quantified regional share).
Interpretation
Market Size Interpretation
With global AI software revenue forecast to reach $407.0 billion by 2028 and e-commerce projected at $1.6 trillion in 2024, the market-size signal for the gift industry is clear as AI investment scales alongside online gifting and personal care, where e-commerce penetration sits at 6.2% of global retail sales in 2024 and the US alone hit $1.6 trillion in retail e-commerce sales in 2023.
02 · Category
User Adoption8 stats
01
56% of shoppers say personalization makes them more likely to shop with a brand (measured consumer response to personalization).
02
63% of customers expect personalization at the time they interact with a brand (measured expectation rate).
03
25% of companies say generative AI will improve customer experience (quantified survey result).
04
AI adoption in marketing is reported at 51% among global marketing leaders (measured adoption).
05
In a 2023 survey, 54% of consumers felt comfortable receiving personalized offers based on their browsing history (quantified comfort level).
06
By 2024, 25% of retailers will use AI for supply chain planning (quantified forward-looking adoption).
07
Google reports that 56% of retailers have integrated at least one AI-based shopping feature (measured integration rate).
08
In 2023, 74% of customer service organizations used AI or planned to use AI (measured adoption/planning).
Interpretation
User Adoption Interpretation
User adoption of AI in the gift industry is accelerating because 63% of customers expect personalization at the moment they interact with a brand and 56% say it makes them more likely to shop, while marketing adoption already stands at 51% among global leaders.
03 · Category
Cost Analysis15 stats
01
AI is expected to reduce marketing costs by 8% to 10% for businesses that effectively deploy it (quantified efficiency estimate).
02
AI-powered fraud detection can reduce chargeback rates by 10% to 25% (quantified risk reduction range).
03
$816 billion of global consumer returns are projected for 2024 (quantified returns volume).
04
Customer data platforms can reduce marketing costs by 10% to 20% through improved targeting (quantified).
05
AI-driven content generation can cut content production time by 40% (quantified time reduction).
06
In 2023, consumers reported $1.4 billion in losses from gift card scams in the US (quantified losses).
07
AI can reduce fulfillment costs by 5% to 10% through route optimization in logistics (quantified cost reduction).
08
Route optimization using AI can reduce fuel consumption by 10% to 15% in logistics operations (quantified fuel reduction).
09
AI-generated product copy reduces edit cycles by 35% (quantified productivity gain).
10
Improved product data quality reduces customer service requests by 10% (quantified impact).
11
AI computer vision can grade product condition to reduce return fraud by 25% (quantified).
12
Fraud losses from online transactions were $44 billion in 2023 globally (quantified).
13
AI-based risk scoring can reduce false positives by 30% (quantified).
14
AI can reduce customer support ticket volume by 20% through self-service (quantified).
15
Assortment optimization reduces dead stock by 10% (quantified).
Interpretation
Cost Analysis Interpretation
Cost-focused AI initiatives in the gift industry are already showing measurable savings and risk reduction, with marketing costs projected to drop by 8% to 20% and fraud-driven chargebacks potentially falling by 10% to 25%.
More related reading
04 · Category
Performance Metrics30 stats
01
20% of retail inventory is affected by demand forecasting errors, highlighting the measurable value-at-stake for AI forecasting in retail operations.
02
Retailers expect AI to improve supply chain operations by up to 15% on average (quantified expected improvement).
03
Up to 30% faster demand planning cycles are reported achievable with AI-enabled planning tools (quantified cycle time improvement).
04
58% of consumers say they are more likely to buy again after a good returns experience (measured loyalty impact).
05
A customer service chatbot can reduce average handling time by 20% (quantified operational efficiency).
06
Email marketing has a median ROI of 36:1 (quantified ROI benchmark), relevant to AI-optimized gifting email campaigns.
07
The average open rate for retail promotional emails is 21% (quantified benchmark).
08
The average click-through rate (CTR) for retail emails is 2.3% (quantified benchmark).
09
Customers are 4.2x more likely to engage with chatbots when offered personalized recommendations (quantified conversion lift).
10
Retailers report that AI image recognition can improve product tagging accuracy by 20 percentage points (quantified improvement).
11
AI-based dynamic pricing can increase revenue by 2% to 8% (quantified revenue uplift).
12
Dynamic pricing reduces stockouts and overstock by improving price-market fit (measured effect: 12% reduction in overstock in a study).
13
Computer vision AI reduces scan errors by 30% in checkout operations (quantified improvement claim).
14
Recommendation personalization increases average cart size by 15% (quantified).
15
AI-powered image search reduces customer effort and increases conversion; measured improvement of 9% in retailer case study (quantified).
16
AI can improve inventory accuracy by 20% to 30% (quantified range).
17
A machine vision approach can reduce picking errors by 25% (quantified).
18
AI chatbots have average containment rates of 20% to 40% in customer service (quantified range).
19
A containment rate of 35% means 35% of chats do not escalate to human agents (measurable chatbot metric definition).
20
In gift retail, customers purchase more when 'occasion' filters are relevant; users exposed to occasion AI saw 18% higher conversion (quantified).
21
Seasonality-driven demand forecasting reduces stockouts by 15% in holiday retail (quantified).
22
Retailers using AI for holiday demand planning reduce planning-to-execution lag by 25% (quantified).
23
AI can improve markdown optimization by 2% to 5% (quantified improvement).
24
AI-based assortment optimization can increase revenue by 1% to 3% (quantified).
25
In a retail ML application, accuracy of demand predictions reached 87% for a modeled dataset (quantified).
26
Retail ML models often report MAE reductions of 15% after feature enrichment (quantified ML metric).
27
In customer churn prediction models used in retail, AUC scores of 0.80 are common (quantified ML metric).
28
A 2021 academic paper on personalization in e-commerce reported a statistically significant conversion uplift of 14.6% from ML recommendations (quantified effect size).
29
A 2019 peer-reviewed study found that recommender systems reduced customer search time by an average of 25% (quantified behavioral effect).
30
A peer-reviewed study reported that conversational agents can increase customer satisfaction scores by 0.4 points on a 5-point scale (quantified).
Interpretation
Performance Metrics Interpretation
In the gift retail performance metrics, AI is showing clear, measurable wins with outcomes like a 15% average supply chain improvement, 30% faster demand planning cycles, and a 36:1 median email marketing ROI, demonstrating that AI can directly enhance operational efficiency and revenue impact.
report visual · Comparison
AI’s strongest reported customer-service impacts (2024)
Across global customer service leaders, AI’s biggest reported effect is boosting support agents’ productivity, leading the other outcomes in the same 2024 set by a clear margin (pr
54% of customer service leaders say AI has increased their support agents’ productivity (2024)54%
49% of customer service leaders say AI has improved customer satisfaction (2024)49%
44% of customer service leaders say AI has reduced operational costs (2024)44%
37% of customer service leaders say AI has improved case-handling time (2024)37%
36% of customer service leaders say AI has improved workload management (2024)36%
31% of customer service leaders say AI has improved first-contact resolution (2024)31%
source-verifiedgartner.com2024
05 · Category
Industry Trends16 stats
01
Generative AI could add $2.6to $4.4 trillion annually to global economy (measurable macro estimate).
02
$1.1to $1.5 trillion annually of that economic potential is projected to come from retail and consumer goods (macro quantified sector).
03
In McKinsey’s estimates, retail uses could create $75to $100 billion in value through marketing and sales optimization (quantified).
04
Visual search adoption grew 150% from 2020 to 2023 in reported analytics from retail tech providers (measured growth rate).
05
By 2025, chatbots will handle 15% of customer service interactions (Gartner quantified forecast).
06
73% of consumers use multiple channels during shopping journeys (quantified omnichannel behavior).
07
94% of retailers say they have customer data but only 30% use it effectively (measured data utilization gap).
08
In Gartner research, marketing organizations will increase spending on AI by 25% in 2024 (quantified budget increase).
09
By 2026, 80% of customer interactions will be handled by AI (Gartner forecast quantified).
10
Mobile devices drove 60% of e-commerce traffic in 2023 (quantified traffic share relevant to mobile gifting).
11
In the US, mobile accounted for about 70% of e-commerce sales in 2023 (quantified).
12
By 2025, 50% of customer interactions in retail will involve AI (forecast).
13
In the US, December e-commerce sales are about 20% of the full-year total in many datasets (quantified month share proxy).
14
US retail sales growth slowed to 3.2% in 2023 (quantified).
15
In 2023, retail e-commerce sales grew 7.6% year-over-year in the US (quantified).
16
In 2022, retail e-commerce sales in the US grew 2.0% year-over-year (quantified).
Interpretation
Industry Trends Interpretation
For the gift industry, Generative AI is projected to add $2.6 to $4.4 trillion to the global economy and $1.1 to $1.5 trillion of that could come from retail and consumer goods, while retail’s push toward smarter discovery and support is already visible as visual search adoption surged 150% from 2020 to 2023 and chatbots are forecast to handle 15% of customer service interactions by 2025.
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Christopher Morgan. (2026, February 13). AI In The Gift Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-gift-industry-statistics
MLA
Christopher Morgan. "AI In The Gift Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-gift-industry-statistics.
Chicago
Christopher Morgan. 2026. "AI In The Gift Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-gift-industry-statistics.
Sources & references
73 datasets cited across this report · attribution is report-level
+42 additional datasets cited (not shown individually)